Data collected from sensors in real-time applications is commonly referred to as time series data, streaming data, and/or data streams, and represents a substantially continuous flow of data. For example, modern industrial facilities often have multiple sensors to gather a wide variety of data types for monitoring the state or condition of various operations at the facility. The streaming data may be analyzed to detect “events” and thus warn of impending failures. By way of illustration, oil and gas production equipment can be highly specialized (and even custom manufactured for a site). Repair and replacement is often expensive, particularly for offshore assets. Early detection and prevention of problems can result in higher production and lower costs.
Oil and gas production equipment is often located in remote areas, offshore, or in extremely hot, cold, or even dangerous environments. For all of these reasons, it is often desired in the oil drilling and production industry to utilize automated surveillance systems to monitor various stages of production and aid the operators with ensuring production with few, if any, interruptions.
The oil and gas industry often equips oil and gas wells with thousands of sensors and gauges to measure flow rates, pressure, and temperature, among other parameters. Any variations in flow rate, pressure and/or temperature may indicate an issue that needs to be addressed in order to avoid a partial or even complete shutdown of the oil well, which can lead to lost productivity and lower profit margins.
But data collected from these sensors can be “noisy,” the data often does not have a constant amplitude, and the data can be plagued by shifts in the mean. These aspects of the data make it difficult to accurately model the data stream and extract relevant events. In addition, quickly detecting changes can be difficult in a real-time or “online” environment, due to the reliance on intensive mathematical analysis which can take significant time to compute.